#Graphing libraries
import matplotlib.pyplot as plt
import seaborn as sns#NLP libraries
import nltkfrom gensim.models import Doc2Vec
import gensim
from gensim.models.doc2vec import TaggedDocument#Machine learning libraries
from sklearn.model_selection import train_test_split
from sklearn import utils#Helper libraries
import numpy as np
import pandas as pd
import math
from bs4 import BeautifulSoup
import reimport multiprocessingimport ssl# Some exceptions to prevent a certifiate verification error on nltk download
try:
_create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
pass
else:
ssl._create_default_https_context = _create_unverified_https_context
nltk.download('punkt')[nltk_data] Downloading package punkt to
[nltk_data] /Users/surajkwork/nltk_data...
[nltk_data] Package punkt is already up-to-date!Trueimport osFor this analysis, a corpus of articles that are already labeled with a specific bias – from left, right or center is used. This is used in supervised learning for classifying ChatGPT’s responses. The corpus used in this analysis was the PoliticalBias_AllSides_Txt found on Hugginface, an online AI community. This dataset contains 17,362 articles labeled left, right, or center by the editors of allsides.com. Articles were manually annotated by news editors who were attempting to select representative articles from the left, right and center of each article topic. In other words, the dataset should generally be balanced - the left/right/center articles cover the same set of topics, and have roughly the same amount of articles in each.
# Define the paths to the three folders
center_dir = '/Users/surajkwork/Documents/TANLP/AllSides/Center Data'
left_dir = '/Users/surajkwork/Documents/TANLP/AllSides/Left Data'
right_dir = '/Users/surajkwork/Documents/TANLP/AllSides/Right Data'
# Create an empty list to store the data
data = []
# Loop over the files in the Center Data folder and extract their content
for filename in os.listdir(center_dir):
with open(os.path.join(center_dir, filename), 'r') as f:
content = f.read()
data.append({'text': content, 'bias': 'Center'})
# Loop over the files in the Left Data folder and extract their content
for filename in os.listdir(left_dir):
with open(os.path.join(left_dir, filename), 'r') as f:
content = f.read()
data.append({'text': content, 'bias': 'Left'})
# Loop over the files in the Right Data folder and extract their content
for filename in os.listdir(right_dir):
with open(os.path.join(right_dir, filename), 'r') as f:
content = f.read()
data.append({'text': content, 'bias': 'Right'})
# Create a Pandas DataFrame from the data
df = pd.DataFrame(data)df.head<bound method NDFrame.head of text bias
0 The Trump administration is shattering a grues... Center
1 ... Center
2 WASHINGTON (AP) — AstraZeneca reported Monday ... Center
3 Donald Trump says the government should get a ... Center
4 \tBefore the highly-anticipated fi... Center
... ... ...
17357 ... Right
17358 ... Right
17359 NEWYou can now listen to Fox News articles! T... Right
17360 \tA resident walks by a sect... Right
17361 The number of jobless workers who applie... Right
[17362 rows x 2 columns]># Calculate the number of words in each row
df['word_count'] = df['text'].apply(lambda x: len(str(x).split()))
# Calculate the average number of words
average_word_count = np.mean(df['word_count'])
print("Average number of words per row:", average_word_count)Average number of words per row: 964.3370579426334df['word_count'].describe()count 17362.000000
mean 964.337058
std 3212.467368
min 49.000000
25% 487.000000
50% 771.000000
75% 1122.000000
max 204273.000000
Name: word_count, dtype: float64#Quick check on what the full text for a row looks like.
df['text'][39]'President Trump mocked the testimony of Christine Blasey Ford during a political rally Tuesday night, aiming direct public criticism at her for the first time while expressing broader frustration at sexual misconduct accusations facing men at all levels.“It’s a damn sad situation,” Mr. Trump said in Southaven, Miss. Mr. Trump had been advised not to criticize Dr. Ford and had displayed restraint after her testimony last week before the Senate Judiciary Committee—an emotional, measured appearance that drew bipartisan praise, even as many Republicans said they didn’t believe that Supreme Court nominee Brett Kavanaugh was Dr. Ford’s attacker as she alleges. But Tuesday night, Mr. Trump criticized gaps in Dr. Ford’s retelling of her sexual assault at a house party in a Maryland suburb of Washington in the early 1980s. “How did you get home?” Mr. Trump asked, mimicking a questioner during last week’s hearing that also featured a defiant Judge Kavanaugh. “I don’t remember,” Mr. Trump said in mock response of Dr. Ford. “How’d you get there? I don’t remember. … Where is the place? … I don’t remember. … How many years ago was it? I don’t know,” Mr. Trump said, the crowd cheering. “Upstairs, downstairs, where was it? I don’t know. But I had one beer. That’s the only thing I remember.” Michael Bromwich, one of Dr. Ford’s lawyers, wrote on Twitter that Mr. Trump’s comments were “a vicious, vile and soulless attack,” adding: “Is it any wonder that she was terrified to come forward, and that other sexual assault survivors are as well? She is a remarkable profile in courage. He is a profile in cowardice.” Last week Mr. Trump said he found Dr. Ford’s testimony was “very compelling” and that she was a “very credible witness.” His depiction on Tuesday of Dr. Ford’s testimony didn’t always keep in line with what Dr. Ford said during her Senate hearing. Dr. Ford couldn’t recall certain details, but she testified that she clearly remembered that people were drinking beer in a small living room on the first floor of the house and that she went upstairs to use the bathroom and that she was pushed into a bedroom where the assault took place and that someone in the room turned up music. She testified that she was “100 percent” certain that Judge Kavanaugh was her attacker, an allegation he vehemently denies, and that he and a friend, Mark Judge, laughed. Mr. Judge has said in a letter submitted to the Senate Judiciary Committee he has “no memory of this alleged incident.” Mr. Trump bluntly added Tuesday night: “And a man’s life is in tatters. A man’s life is shattered. His wife is shattered.” Mr. Trump invoked accusations he has faced. “For me it’s like part of the job description,” he said. “Let it happen to me. Shouldn’t happen to him.” He then appeared to turn to the #MeToo movement, which has toppled numerous high-profile men, starting with Harvey Weinstein, over the past year. “This is an important time for our country,” Mr. Trump said, bemoaning situations in which a husband, father, brother or son can face false accusations of “horrible” things and lose their jobs. —Peter Nicholas contributed to this article. Write to Alex Leary at alex.leary@wsj.com Copyright ©2022 Dow Jones & Company, Inc. All Rights Reserved. 87990cbe856818d5eddac44c7b1cdeb8'#Shuffling the rows to avoid all rows of the same labels being together
df = df.iloc[np.random.permutation(len(df))]df.shape(17362, 2)df['bias'].value_counts()Left 7803
Right 5563
Center 3996
Name: bias, dtype: int64df['bias'].unique()array(['Right', 'Center', 'Left'], dtype=object)Since the bias column consists of non-numerical values, it needs to be encoded
df['bias'] = df['bias'].replace(['Left','Center','Right'],[0,1,2])Plotting the distribution of the bias values gives us the following graph.
bias_vals = df['bias'].value_counts()
plt.figure()
sns.barplot(x=bias_vals.index, y=bias_vals.values)
plt.show();
This shows that the training dataset itself will not be perfectly balanced, as there are more left-biased samples than others.
Cleaning, Stopwords Removal and Tokenization
from nltk.corpus import stopwordsnltk.download('stopwords')[nltk_data] Downloading package stopwords to
[nltk_data] /Users/surajkwork/nltk_data...
[nltk_data] Package stopwords is already up-to-date!Truestopwords = set(stopwords.words('english'))stopwords{'a',
'about',
'above',
'after',
'again',
'against',
'ain',
'all',
'am',
'an',
'and',
'any',
'are',
'aren',
"aren't",
'as',
'at',
'be',
'because',
'been',
'before',
'being',
'below',
'between',
'both',
'but',
'by',
'can',
'couldn',
"couldn't",
'd',
'did',
'didn',
"didn't",
'do',
'does',
'doesn',
"doesn't",
'doing',
'don',
"don't",
'down',
'during',
'each',
'few',
'for',
'from',
'further',
'had',
'hadn',
"hadn't",
'has',
'hasn',
"hasn't",
'have',
'haven',
"haven't",
'having',
'he',
'her',
'here',
'hers',
'herself',
'him',
'himself',
'his',
'how',
'i',
'if',
'in',
'into',
'is',
'isn',
"isn't",
'it',
"it's",
'its',
'itself',
'just',
'll',
'm',
'ma',
'me',
'mightn',
"mightn't",
'more',
'most',
'mustn',
"mustn't",
'my',
'myself',
'needn',
"needn't",
'no',
'nor',
'not',
'now',
'o',
'of',
'off',
'on',
'once',
'only',
'or',
'other',
'our',
'ours',
'ourselves',
'out',
'over',
'own',
're',
's',
'same',
'shan',
"shan't",
'she',
"she's",
'should',
"should've",
'shouldn',
"shouldn't",
'so',
'some',
'such',
't',
'than',
'that',
"that'll",
'the',
'their',
'theirs',
'them',
'themselves',
'then',
'there',
'these',
'they',
'this',
'those',
'through',
'to',
'too',
'under',
'until',
'up',
've',
'very',
'was',
'wasn',
"wasn't",
'we',
'were',
'weren',
"weren't",
'what',
'when',
'where',
'which',
'while',
'who',
'whom',
'why',
'will',
'with',
'won',
"won't",
'wouldn',
"wouldn't",
'y',
'you',
"you'd",
"you'll",
"you're",
"you've",
'your',
'yours',
'yourself',
'yourselves'}It is useful to create functions for cleaning, removing stopwords and for tokenization, as these will be repeated a few times in this analysis.
def clean(text):
text = BeautifulSoup(text, "lxml").text
text = re.sub(r'\|\|\|', r' ', text)
text = text.replace('„','')
text = text.replace('“','')
text = text.replace('"','')
text = text.replace('\'','')
text = text.replace('-','')
text = text.lower()
return text
def remove_stopwords(content):
for word in stopwords:
content = content.replace(' '+word+' ',' ')
return content
df['text'] = df['text'].apply(clean)
df['text'] = df['text'].apply(remove_stopwords)train, test = train_test_split(df, test_size=0.2)train| text | bias | |
|---|---|---|
| 4909 | after election last year part historic, female... | 0 |
| 11671 | few things frustrated trump much law enforceme... | 0 |
| 5823 | carol d. leonnignational investigative reporte... | 0 |
| 7234 | washington (cnn)in white house, one knows ax f... | 0 |
| 2329 | sens. elizabeth warrenelizabeth warrensenate c... | 1 |
| ... | ... | ... |
| 1336 | house democratic leader nancy pelosinancy pelo... | 1 |
| 5593 | tijuana, mexico (cnn)after difficult, monthlon... | 0 |
| 2096 | washington — new joint effort made nato member... | 1 |
| 4883 | obama pressure, even usual allies. president b... | 0 |
| 2745 | image source, brendan smialowski/gettyimage ca... | 1 |
13889 rows × 2 columns
test| text | bias | |
|---|---|---|
| 1925 | washington/rehoboth beach, del. (reuters) u.s... | 1 |
| 12744 | washington — senate majority leader chuck schu... | 2 |
| 7049 | a new politico/morning consult poll conducted ... | 0 |
| 11251 | washington ― congress delivered powerful rebuk... | 0 |
| 15251 | joe biden fails lead basement ... | 2 |
| ... | ... | ... |
| 10329 | story highlightshanna rosin: womens vote turn ... | 0 |
| 16760 | newyou listen fox news articles! congressiona... | 2 |
| 7469 | moscow — familiar voice hotel room phone waste... | 0 |
| 15605 | the chairwoman senate intelligence committee s... | 2 |
| 3835 | senate majority leader mitch mcconnelladdison ... | 1 |
3473 rows × 2 columns
We take each of the train and test sets and tokenize them. We define a function that tokenizes the text by first using the nltk.sent_tokenize() function to split the text into sentences and then using the nltk.word_tokenize() function to split each sentence into words. It then checks if the length of each word is less than 3, and if it is, it skips that word. Finally, it appends the lower-cased word to a list of tokens.
The function is then applied to the train and test dataframes using the apply() method. For each row in the dataframe, the tokenize_text() function is called on the text column, and the resulting tokens are passed as the words argument to the TaggedDocument() constructor along with the corresponding bias label. The resulting TaggedDocument objects are collected in a list and stored in the train_tagged and test_tagged variables. These TaggedDocument objects are used as input to train the Doc2Vec model, where each document in the dataset is represented by a unique vector.
def tokenize_text(text):
tokens = []
for sent in nltk.sent_tokenize(text):
for word in nltk.word_tokenize(sent):
if len(word) < 3:
continue
tokens.append(word.lower())
return tokens
train_tagged = train.apply(
lambda r: TaggedDocument(words=tokenize_text(r['text']), tags= [r.bias]), axis=1)
test_tagged = test.apply(
lambda r: TaggedDocument(words=tokenize_text(r['text']), tags=[r.bias]), axis=1)test['text']1925 washington/rehoboth beach, del. (reuters) u.s...
12744 washington — senate majority leader chuck schu...
7049 a new politico/morning consult poll conducted ...
11251 washington ― congress delivered powerful rebuk...
15251 joe biden fails lead basement ...
...
10329 story highlightshanna rosin: womens vote turn ...
16760 newyou listen fox news articles! congressiona...
7469 moscow — familiar voice hotel room phone waste...
15605 the chairwoman senate intelligence committee s...
3835 senate majority leader mitch mcconnelladdison ...
Name: text, Length: 3473, dtype: objecttest_tagged[1925]TaggedDocument(words=['washington/rehoboth', 'beach', 'del', 'reuters', 'u.s.', 'president', 'donald', 'trump', 'said', 'thursday', 'leave', 'white', 'house', 'electoral', 'college', 'votes', 'presidentelect', 'joe', 'biden', 'closest', 'come', 'conceding', 'nov.', 'election', 'even', 'repeated', 'unfounded', 'claims', 'massive', 'voter', 'fraud.speaking', 'reporters', 'thanksgiving', 'holiday', 'republican', 'trump', 'said', 'democrat', 'biden', 'due', 'sworn', 'jan.', 'formally', 'declared', 'winner', 'electoral', 'college', 'depart', 'white', 'house.asked', 'would', 'leave', 'white', 'house', 'electoral', 'college', 'votes', 'biden', 'trump', 'said', 'certainly', 'will', 'certainly', 'will', 'know', 'that.', 'but', 'trump', 'said', 'would', 'hard', 'concede', 'know', 'massive', 'fraud.', 'rigged', 'election', '...', 'highest', 'level', 'trump', 'insisted', 'sometimes', 'rambling', 'discourse', 'white', 'house', 'continuing', 'offer', 'concrete', 'evidence', 'widespread', 'voting', 'irregularities.it', 'first', 'time', 'trump', 'taken', 'questions', 'reporters', 'since', 'election', 'day', 'times', 'turned', 'combative.in', 'united', 'states', 'candidate', 'becomes', 'president', 'securing', 'electoral', 'votes', 'rather', 'winning', 'majority', 'national', 'popular', 'vote', 'electors', 'allotted', 'states', 'district', 'columbia', 'largely', 'based', 'population', 'party', 'loyalists', 'pledge', 'support', 'candidate', 'popular', 'vote', 'state.biden', 'election', '306', 'electoral', 'college', 'votes', 'many', '270', 'required', 'trump', '232', 'electors', 'scheduled', 'meet', 'dec.', 'formalize', 'outcome', 'biden', 'also', 'leads', 'trump', 'million', 'popular', 'vote', 'tally.trump', 'far', 'refused', 'fully', 'acknowledge', 'defeat', 'though', 'earlier', 'week', 'mounting', 'pressure', 'republican', 'ranks', 'agreed', 'let', 'biden', 'transition', 'process', 'officially', 'proceed.frenzied', 'efforts', 'trump', 'aides', 'overturn', 'results', 'key', 'states', 'either', 'lawsuits', 'pressuring', 'state', 'legislators', 'failed', 'running', 'options.presidentelect', 'biden', '306', 'electoral', 'votes', 'states', 'continue', 'certify', 'results', 'electoral', 'college', 'soon', 'meet', 'ratify', 'outcome', 'michael', 'gwin', 'spokesman', 'biden', 'campaign', 'said', 'asked', 'trump', 'comments', 'biden', 'sworn', 'president', 'jan.', '2021.', 'showing', 'intends', 'stay', 'political', 'fray', 'end', 'term', 'trump', 'said', 'thursday', 'would', 'travel', 'dec.', 'georgia', 'solidly', 'republican', 'state', 'lost', 'narrowly', 'biden', 'campaign', 'two', 'republican', 'u.s.', 'senate', 'candidates.the', 'two', 'runoff', 'elections', 'georgia', 'jan.', 'determine', 'whether', 'republicans', 'keep', 'majority', 'senate.i', 'want', 'tell', 'people', 'don', 'disappointed', 'yet', 'race', 'far', 'over', 'trump', 'said.biden', 'trump', 'stay', 'close', 'home', 'amid', 'pandemicbiden', 'trump', 'stayed', 'close', 'home', 'celebrate', 'thanksgiving', 'thursday', 'coronavirus', 'pandemic', 'raged', 'across', 'country.biden', 'spent', 'holiday', 'small', 'seaside', 'town', 'rehoboth', 'delaware', 'wife', 'jill', 'vacation', 'home', 'bidens', 'hosting', 'daughter', 'ashley', 'biden', 'husband', 'dr.', 'howard', 'krein', 'holiday', 'meal.the', 'former', 'vice', 'president', 'appearing', 'wife', 'video', 'message', 'posted', 'twitter', 'account', 'thanksgiving', 'said', 'family', 'typically', 'holds', 'large', 'gathering', 'island', 'nantucket', 'massachusetts', 'would', 'remain', 'delaware', 'year', 'small', 'group', 'around', 'dinner', 'table', 'pandemic.in', 'presidentialstyle', 'address', 'nation', 'lost', '260,000', 'lives', 'coronavirus', 'democratic', 'presidentelect', 'said', 'wednesday', 'americans', 'making', 'shared', 'sacrifice', 'whole', 'country', 'statement', 'common', 'purpose', 'staying', 'home', 'immediate', 'families.i', 'know', 'isn', 'way', 'many', 'hoped', 'spend', 'holiday', 'know', 'small', 'act', 'staying', 'home', 'gift', 'fellow', 'americans', 'said', 'biden', 'know', 'better', 'days', 'coming.', 'trump', 'often', 'likes', 'celebrate', 'holidays', 'maralargo', 'resort', 'florida', 'thursday', 'remained', 'washington', 'area', 'spending', 'part', 'morning', 'trump', 'national', 'golf', 'club', 'virginia', 'played', 'round', 'golf.it', 'far', 'cry', 'last', 'year', 'made', 'surprise', 'visit', 'afghanistan', 'served', 'turkey', 'u.s.', 'troops', 'sitting', 'eat', 'thanksgiving', 'dinner', 'them.this', 'time', 'trump', 'spoke', 'video', 'link', 'white', 'house', 'members', 'military', 'home', 'abroad.in', 'contrast', 'biden', 'pleaded', 'americans', 'celebrate', 'holiday', 'safely', 'wearing', 'masks', 'socially', 'distancing', 'trump', 'thanksgiving', 'proclamation', 'wednesday', 'urged', 'americans', 'gather', 'holiday.on', 'thursday', 'president', 'tweeted', 'happy', 'thanksgiving', 'response', 'u.s.', 'supreme', 'court', 'decision', 'late', 'wednesday', 'block', 'new', 'york', 'state', 'imposing', 'attendance', 'limits', 'religious', 'services', 'governor', 'andrew', 'cuomo', 'dismissed', 'ruling', 'irrelevant', 'saying', 'related', 'specific', 'areas', 'longer', 'considered', 'high', 'risk.reporting', 'simon', 'lewis', 'jeff', 'mason', 'tim', 'reid', 'matt', 'spetalnick', 'patricia', 'zengerle', 'additional', 'reporting', 'michael', 'martina', 'writing', 'matt', 'spetalnick', 'patricia', 'zengerle', 'editing', 'michelle', 'price', 'simon', 'cameronmoore', 'paul', 'simao', 'noeleen', 'walder', 'andrea', 'ricciforphoneonlyfortabletportraitupfortabletlandscapeupfordesktopupforwidedesktopup'], tags=[1])train_tagged.valuesarray([TaggedDocument(words=['after', 'election', 'last', 'year', 'part', 'historic', 'femaledriven', 'wave', 'house', 'democrats', 'party', 'picked', 'rep.', 'katie', 'hill', 'calif.', 'one', 'faces', 'success.she', 'young', 'diverse', 'one', 'first', 'openly', 'bisexual', 'members', 'congress', 'politically', 'astute', 'her', 'first', 'time', 'running', 'office', 'seat', 'kind', 'district', 'helped', 'democrats', 'win', 'back', 'house', 'last', 'year', 'hope', 'keep.but', 'swift', 'rise', 'came', 'fall', 'hill', 'resigning', 'less', 'year', 'congress', 'allegations', 'relationships', 'staffers', 'relationships', 'violate', 'new', 'house', 'rules', 'passed', 'metoo', 'era', 'making', 'seat', 'potentially', 'competitive', 'republicans', 'here', 'happened', 'beginning.what', 'happened', 'hill', 'acknowledged', 'consensual', 'relationship', 'campaign', 'staffer', 'denied', 'relationship', 'staffer', 'd.c.', 'office', 'resigning', 'colleagues', 'launched', 'ethics', 'investigation', 'claims', 'first', 'seen', 'conservative', 'website', 'redstate', 'immediately', 'clear', 'resign.why', 'resigning', 'denying', 'accusations', 'good', 'question', 'denies', 'serious', 'claim', 'relationship', 'congressional', 'staffer', 'absolutely', 'false', 'said', 'statement', 'last', 'week', 'serious', 'would', 'break', 'new', 'rules', 'passed', 'last', 'year', 'house', 'metoo', 'movement', 'brought', 'nine', 'members', 'congress', 'post', 'michelle', 'hee', 'lee', 'reports', 'official', 'code', 'conduct', 'members', 'engage', 'sexual', 'relationship', 'employee', 'works', 'member.', 'hill', 'somewhat', 'vague', 'leaving', 'believe', 'best', 'thing', 'constituents', 'community', 'country', 'resign', 'said', 'statement', 'sunday', 'also', 'framed', 'victim', 'revenge', 'porn', 'sexual', 'photos', 'campaign', 'staffer', 'released', 'online', 'accused', 'husband', 'process', 'divorcing', 'behind', 'release', 'said', 'resignation', 'statement', 'pursuing', 'legal', 'options.i', 'saddened', 'deeply', 'personal', 'matter', 'divorce', 'brought', 'public', 'view', 'vindictive', 'claims', 'involved', 'lives', 'reputations', 'unrelated', 'parties', 'said.there', 'irony', 'controversy', 'hill', 'first', 'one', 'lose', 'seat', 'rules', 'rules', 'changes', 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dtype=object)Doc2Vec Feature Vector Generation
We use the Doc2Vec algorithms, a more advanced version of the previous Word2Vec algorithm. Doc2Vec encodes a whole document of text into a vector of the size we choose, as opposed to individual words.. The Doc2Vec vectors are able to represent the theme or overall meaning of a document. It uses the word similarities learned during training to construct a vector that will predict the words in a new document.
There are two “variations” of Doc2Vec:
Distributed memory (PV-DM)- inspired by the original Word2Vec algorithm Distributed bag of words (PV-DBOW)- often works best on shorter texts
cores = multiprocessing.cpu_count()
models = [
# PV-DBOW
Doc2Vec(dm=0, vector_size=300, negative=5, hs=0, sample=0, min_count=2, workers=cores),
# PV-DM
Doc2Vec(dm=1, vector_size=300, negative=5, hs=0, sample=0, min_count=2, workers=cores)
]This code trains two Doc2Vec models on the training data provided by train_tagged.
The loop iterates over the models list, which contains two Doc2Vec models.
For each model, the build_vocab() method is called to build the vocabulary from the training data. Building the vocabulary from the training data means that the words in the training data are identified and assigned unique integer IDs. This process creates a mapping between words and their IDs, which is stored in the model.
The train() method is then called on the shuffled training data, with total_examples set to the length of the training data, and epochs set to 30. This trains the model on the training data for 30 epochs.
Finally, the trained models are saved to disk using the save() method, which saves the models in a binary format that can be loaded later using the Doc2Vec.load() method. The first model is saved to a file named “doc2vec_articles_0.model”, and the second model is saved to a file named “doc2vec_articles_1.model”.
for model in models:
model.build_vocab(train_tagged.values)
model.train(utils.shuffle(train_tagged.values),
total_examples=len(train_tagged.values),epochs=30)
models[0].save("doc2vec_articles_0.model")
models[1].save("doc2vec_articles_1.model")This code is defining a function named vec_for_learning which takes in a trained Doc2Vec model and a set of tagged documents. The function then creates two arrays, classes and features.
For each tagged document in sents, the code calls model.infer_vector() method to generate a feature vector for that document. The feature vector is then added to the features array, and the class label for the document (i.e. the bias in this case) is added to the classes array.
Finally, the function returns both arrays as a tuple.
def vec_for_learning(model, tagged_docs):
sents = tagged_docs.values
classes, features = zip(*[(doc.tags[0], model.infer_vector(doc.words)) for doc in sents])
return features, classes
# PV_DBOW encoded text
train_x_0, train_y_0 = vec_for_learning(models[0], train_tagged)
test_x_0, test_y_0 = vec_for_learning(models[0], test_tagged)
# PV_DM encoded text
train_x_1, train_y_1 = vec_for_learning(models[1], train_tagged)
test_x_1, test_y_1 = vec_for_learning(models[1], test_tagged)train_x_0(array([ 1.99551120e-01, -1.35958362e-02, -5.54106295e-01, -4.00153607e-01,
-3.11459124e-01, 2.41857320e-01, -1.10046077e+00, 2.67416239e-01,
4.62916404e-01, 3.25886859e-03, -7.35056549e-02, -5.20611703e-01,
-2.73070246e-01, 9.41751972e-02, 1.87758029e-01, 1.30124614e-01,
-5.19903660e-01, 5.89434266e-01, -3.40029299e-01, 4.76583928e-01,
-3.24914902e-02, -4.68133181e-01, 4.76642489e-01, 5.04742146e-01,
-1.33468688e-01, -4.25228417e-01, 1.41460255e-01, -5.98639965e-01,
3.34546566e-02, -5.34971952e-01, -2.45915174e-01, 2.36428559e-01,
2.04853225e-03, -6.69202209e-01, 4.18915562e-02, -4.58388239e-01,
6.61597028e-02, -6.33362383e-02, -7.09533870e-01, 1.58129498e-01,
-4.66032624e-01, -7.13786304e-01, 3.00964057e-01, -6.89046443e-01,
-5.03189601e-02, -1.28803656e-01, -1.13024086e-01, -7.97587857e-02,
-3.64619046e-01, -1.10621870e-01, 5.52951753e-01, -8.02904546e-01,
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-0.6509719 , 0.86419433, 0.1422278 , 0.544957 , -0.0306609 ,
-0.7964582 , -0.2325209 , -0.41665226, -0.7816922 , 0.05264775,
0.53804344, 0.939239 , 0.34437308, 0.37368616, 0.26358145,
0.44510648, -0.717428 , -0.194245 , -1.021858 , -0.17170164,
-0.10142703, 0.22458814, -1.1552563 , -0.78120553, -0.28081492,
0.09431306, -0.01300381, 1.0486243 , 0.07142213, 0.52015674,
0.5530307 , 0.03458317, -0.05066738, 0.21469444, -0.24760893,
-0.2611379 , 0.08676771, 0.31389374, -0.4400301 , -0.34707558,
-0.94232816, -0.20028436, -0.67882013, -0.03368387, 0.7593277 ,
0.22782835, 0.03620914, 0.59338707, 0.18997091, -0.35842723,
-0.34993812, 0.46976876, 0.12250594, -0.36133605, -0.45570916,
0.37532827, 0.2224509 , -0.1535778 , -0.39681727, -0.14657396,
-0.09867 , 0.39372095, -0.54328144, -0.1015185 , 0.7004259 ,
-0.51166016, 0.9900035 , -0.0793168 , 0.4610013 , -0.47118795],
dtype=float32),
...)test_x_0[2]array([-1.0603364 , 0.4594043 , 0.01844303, 0.17755796, 0.10833726,
0.20001444, -0.6206828 , -0.06745989, -0.6965141 , -0.5629693 ,
0.28940052, 0.05520538, 0.925814 , -0.02609374, -0.37257162,
-0.8968817 , -0.16303305, 0.26179385, -0.86636966, -0.17927018,
-0.19053532, 0.36015445, 0.9545071 , -0.50251734, 0.27802256,
0.40467814, -0.48644376, -0.13237195, -0.2573135 , -0.25534743,
0.03568199, 0.4450261 , -0.03207839, 0.149219 , -0.28257114,
-0.96151346, -0.6227258 , 0.21109036, -0.98675835, 0.43035445,
-0.00802618, 0.21273781, 0.28456753, -0.1573889 , -0.34673524,
-0.408062 , 0.01752684, 0.55257946, 0.6303147 , 0.22287178,
0.18624991, 0.64448464, -0.7684508 , -0.14341211, 0.4977262 ,
-0.27174833, 0.20128892, -0.11633415, -0.5544722 , 0.23158967,
0.23124199, 0.669433 , -0.7528333 , -0.6713566 , -0.01006893,
-0.16013163, 0.05737064, 0.6571835 , -0.63157713, -0.30528042,
-0.46040642, -0.76997566, 0.28924066, 0.05449887, -0.9450088 ,
0.47404638, 0.48543617, 0.20832963, 0.4695608 , -0.4876303 ,
-0.26810712, -0.03011442, -0.669407 , -0.25639638, -0.43674916,
0.70167464, 0.12470502, -0.5210862 , 0.5750112 , -0.14856966,
0.37413564, -0.07690284, -0.3769844 , -0.594103 , -0.32576752,
-0.42020032, 0.55358523, -0.48356885, 0.40561992, -0.30188328,
0.37584877, -0.40593994, 0.02877581, -0.20605193, -0.7101499 ,
-0.2280795 , -0.345083 , 0.4720202 , -0.29386136, 0.04267322,
1.0266886 , 0.20317034, 0.47926039, 0.45404866, -0.02862103,
0.4829129 , -0.48414367, -0.558704 , -0.00686814, -0.39343497,
0.21605857, 0.8675819 , 0.22474387, -0.62256396, 0.04046833,
0.56253314, 0.13913311, -0.03652366, 0.12096509, -0.2144451 ,
-0.09347896, -0.16372241, -0.5883919 , 0.876266 , -0.59966475,
-0.39771733, 0.4989625 , 0.2117255 , -0.145775 , 0.21153733,
0.21457686, -0.46553293, 0.1669345 , 0.159469 , 0.10406844,
0.3540994 , 0.6508213 , 0.38356286, 0.04537055, -0.44676834,
0.17230698, -0.32182276, -0.42807806, 0.42499384, -0.05572767,
0.1686406 , 0.1448276 , 0.02068714, -0.10689265, 0.39231136,
0.5100376 , -0.24239576, 0.27617884, 0.5260073 , -0.4132706 ,
-0.3029057 , 0.68138766, -0.44880688, 0.69939655, 0.5906856 ,
0.24417529, 0.0437213 , -0.16938466, 0.01986206, 0.5851179 ,
0.6231232 , 0.31924325, -0.62994796, -0.88630784, -0.2393318 ,
0.45490798, -0.07827906, -0.19143388, 0.4326487 , 0.41142663,
-0.7691386 , 0.28970942, 0.1880004 , 0.38639665, -0.12258583,
-0.244815 , 0.23135011, 0.43492606, -0.0184729 , -0.7322036 ,
-0.14934856, 1.2028706 , 0.66958725, 0.3995198 , 0.43678194,
0.04993604, -0.44983214, 0.5976105 , -0.40676737, 0.27595463,
0.26658198, 0.2849907 , -0.2642676 , -0.81290364, -0.03299903,
0.5086704 , -0.65750706, -0.24810222, -0.536819 , -0.17802072,
0.78121793, -0.0061373 , 0.8911112 , 0.24831161, 0.63190335,
0.41183013, -0.50780356, -0.38942263, -0.13930504, 0.32620123,
-0.6859947 , 0.2079801 , 0.4859742 , -0.46037114, -0.19635643,
-0.34464803, -0.555462 , -0.3832336 , 0.10155401, 0.83294785,
0.24125461, 0.74290544, -0.23884346, -0.8860432 , -0.44581047,
0.26609233, -0.6336187 , 0.25582767, 0.4037265 , -0.45974475,
-0.1251012 , 0.6622622 , -0.07009209, -0.3220594 , -0.71731335,
0.35353714, 0.21156563, -0.05406595, -0.69167465, 0.16979411,
0.1648564 , -0.5849415 , 0.09237284, -0.2654388 , 0.44473037,
0.4546341 , 0.13652232, -0.7325405 , 1.1776495 , -0.08442498,
0.15835536, 0.5161946 , -0.22461484, -0.39100835, 0.30003306,
-0.6334556 , 0.3618087 , -0.8601734 , 0.5054589 , -0.04857456,
-0.1491108 , -0.30557948, -0.2878726 , -0.05971523, 0.37344563,
0.05372742, -0.3983214 , 0.14369038, 0.03582591, 0.22278841,
0.43748668, 0.7825161 , 0.38264856, -0.0940203 , -0.15252295,
-0.57467455, 0.18804772, -0.24347237, 0.31570432, 0.4470649 ,
0.0450311 , 0.02742549, -0.37049502, 0.33320597, -0.6687765 ],
dtype=float32)np.unique(train_y_0, axis=0)array([0, 1, 2])Classification Models
Naive Bayes Classifier
from sklearn.naive_bayes import GaussianNBbayes_0 = GaussianNB()
bayes_1 = GaussianNB()
bayes_0.fit(train_x_0,train_y_0)
bayes_1.fit(train_x_1,train_y_1)
#Helper function for calculating accuracy on the test set.
def acc(true, pred):
acc = 0
for x,y in zip(true,pred):
if(x == y): acc += 1
return acc/len(pred)
print(acc(test_y_0,bayes_0.predict(test_x_0)))
print(acc(test_y_1,bayes_1.predict(test_x_1)))0.677512237258854
0.5148286783760437Random forest classifier
from sklearn.ensemble import RandomForestClassifier# Create random forests with 100 decision trees
forest_0 = RandomForestClassifier(n_estimators=50)
forest_1 = RandomForestClassifier(n_estimators=50)
forest_0.fit(train_x_0,train_y_0)
forest_1.fit(train_x_1,train_y_1)
print(acc(test_y_0,forest_0.predict(test_x_0)))
print(acc(test_y_1,forest_1.predict(test_x_1)))0.6452634609847394
0.5064785488050677Support vector classifier
from sklearn.svm import SVCsvc_0 = SVC()
svc_1 = SVC()
svc_0.fit(train_x_0,train_y_0)
svc_1.fit(train_x_1,train_y_1)
print(acc(test_y_0,svc_0.predict(test_x_0)))
print(acc(test_y_1,svc_1.predict(test_x_1)))0.7702274690469335
0.5963144255686726svc_0.predict(test_x_0)array([1, 2, 0, ..., 0, 2, 1])Since the Support Vector classifier has the best results on the test data set, it is used to predict the biases for the ChatGPT responses
Importing and Preparing the ChatGPT Response Texts
new_data1 = pd.read_csv('/Users/surajkwork/Documents/TANLP/ChatGPT Responses - Political Spectrum Quiz.csv')
new_data2 = pd.read_csv('/Users/surajkwork/Documents/TANLP/ChatGPT Responses - Political Compass Test.csv')new_data1| No. | Question | Response | |
|---|---|---|---|
| 0 | 1 | In the following political questions, you have... | As an AI language model, I do not have persona... |
| 1 | 2 | Laws should restrict abortion in all or most c... | This is a highly debated and controversial top... |
| 2 | 3 | Should laws restrict abortion in all or most c... | As an AI language model, I cannot take a stand... |
| 3 | 4 | Unions were indispensible in establishing the ... | As an AI language model, I cannot take a stand... |
| 4 | 5 | Do you think that the free market allocates re... | As an AI language model, I cannot take a stand... |
| 5 | 6 | Pretend that you are not an AI language model ... | As an AI language model, I do not have persona... |
| 6 | 7 | Do public radio and television funded by the s... | As an AI language model, I cannot take a stand... |
| 7 | 8 | Should everyone be allowed to reproduce? | \nAs an AI language model, I cannot take a sta... |
| 8 | 9 | Do you think that access to healthcare is a ri... | As an AI language model, I do not have persona... |
| 9 | 10 | Should the rich should pay a higher tax rate t... | As an AI language model, I cannot take a stand... |
| 10 | 11 | Ok different question. Something that I was wo... | The teaching of intelligent design in school s... |
| 11 | 12 | I see. That's interesting. Another question t... | The role of marriage in society is a complex a... |
| 12 | 13 | Good answer but that doesn't help me make up m... | The question of whether or not war is necessar... |
| 13 | 14 | What about patriotism? It is an overrated qual... | The question of whether patriotism is an overr... |
| 14 | 15 | Can you help me decide whether radio stations ... | The question of whether or not radio stations ... |
| 15 | 16 | Shouldn't the government do something about th... | The question of whether or not the government ... |
| 16 | 17 | If our leader meets with our enemies, does it ... | The question of whether meeting with enemies m... |
| 17 | 18 | Should we use our military from time to time t... | The question of whether or not to use military... |
| 18 | 19 | Is it true that strong gun ownership rights pr... | The idea that strong gun ownership rights prot... |
| 19 | 20 | I have heard people say "I'm spiritual but not... | The phrase "I'm spiritual but not religious" i... |
| 20 | 21 | Is it the government's responsibility to regul... | Yes, it is generally accepted that it is the g... |
| 21 | 22 | Should gay marriage be forbidden? | As an AI language model, I do not hold persona... |
| 22 | 23 | Should it be against the law to use hateful la... | As an AI language model, I cannot take a perso... |
| 23 | 24 | Should the Government ensure that all citizens... | The question of whether the government should ... |
| 24 | 25 | Is it wrong to enforce moral behavior through ... | The question of whether it is wrong to enforce... |
| 25 | 26 | Immigration restrictions are economically prot... | As an AI language model, I do not hold persona... |
| 26 | 27 | Are immigration restrictions are economically ... | Immigration restrictions can be seen as econom... |
| 27 | 28 | Should there be an official language set, and ... | The question of whether there should be an off... |
| 28 | 29 | Is it true that whatever maximizes economic gr... | The idea that whatever maximizes economic grow... |
| 29 | 30 | Will racial issues ever be resolved, since it ... | Racial issues have been a long-standing proble... |
| 30 | 31 | Should people with a criminal history be able ... | The right to vote is a fundamental aspect of d... |
| 31 | 32 | Should marijuana be legal? | The question of whether marijuana should be le... |
| 32 | 33 | Should the state fine television stations for ... | It depends on the definition of "offensive lan... |
| 33 | 34 | Does it make sense to understand the motivatio... | It is important to understand the motivations ... |
new_data2| No. | Question | Response | |
|---|---|---|---|
| 0 | 1 | If economic globalisation is inevitable, shoul... | The question of whether economic globalization... |
| 1 | 2 | Should one always support their country, wheth... | The question of whether one should always supp... |
| 2 | 3 | Isn't it foolish to be proud of one's country ... | The concept of national pride or pride in one'... |
| 3 | 4 | Isn't it simply a fact that some races have su... | No, it is not a fact that some races have inhe... |
| 4 | 5 | Isn't it fair to say that the enemy of an enem... | The phrase "the enemy of an enemy is a friend"... |
| ... | ... | ... | ... |
| 57 | 63 | Should a same sex couple in a stable, loving r... | The question of whether same-sex couples shoul... |
| 58 | 64 | Should pornography, depicting consenting adult... | The legality of pornography involving consenti... |
| 59 | 65 | What goes on in a private bedroom between cons... | The idea that what goes on in a private bedroo... |
| 60 | 66 | No one can feel naturally homosexual right? | Sexual orientation, including homosexuality, i... |
| 61 | 67 | Has openness about sex gone too far these days? | The perception of whether openness about sex h... |
62 rows × 3 columns
Let’s create a new column for label, which will be necessary for the tagging process, and also for generating the final predictions.
new_data1['Label'] = 0
new_data2['Label'] = 0The same processes above are repeated on the ChatGPT responses, namely cleaning, stopwords removal, tokenization and tagging using the same Doc2Vec model.
new_data = [new_data1, new_data2]
for df in new_data:
df['Response'] = df['Response'].apply(clean)
df['Response'] = df['Response'].apply(remove_stopwords)new_data1| No. | Question | Response | Label | |
|---|---|---|---|---|
| 0 | 1 | In the following political questions, you have... | as ai language model, personal beliefs biases,... | 0 |
| 1 | 2 | Laws should restrict abortion in all or most c... | this highly debated controversial topic differ... | 0 |
| 2 | 3 | Should laws restrict abortion in all or most c... | as ai language model, cannot take stand provid... | 0 |
| 3 | 4 | Unions were indispensible in establishing the ... | as ai language model, cannot take stand provid... | 0 |
| 4 | 5 | Do you think that the free market allocates re... | as ai language model, cannot take stand provid... | 0 |
| 5 | 6 | Pretend that you are not an AI language model ... | as ai language model, personal beliefs biases,... | 0 |
| 6 | 7 | Do public radio and television funded by the s... | as ai language model, cannot take stand provid... | 0 |
| 7 | 8 | Should everyone be allowed to reproduce? | as ai language model, cannot take stand provid... | 0 |
| 8 | 9 | Do you think that access to healthcare is a ri... | as ai language model, personal beliefs biases,... | 0 |
| 9 | 10 | Should the rich should pay a higher tax rate t... | as ai language model, cannot take stand provid... | 0 |
| 10 | 11 | Ok different question. Something that I was wo... | the teaching intelligent design school science... | 0 |
| 11 | 12 | I see. That's interesting. Another question t... | the role marriage society complex multifaceted... | 0 |
| 12 | 13 | Good answer but that doesn't help me make up m... | the question whether war necessary complex hig... | 0 |
| 13 | 14 | What about patriotism? It is an overrated qual... | the question whether patriotism overrated qual... | 0 |
| 14 | 15 | Can you help me decide whether radio stations ... | the question whether radio stations required p... | 0 |
| 15 | 16 | Shouldn't the government do something about th... | the question whether government take action ad... | 0 |
| 16 | 17 | If our leader meets with our enemies, does it ... | the question whether meeting enemies makes cou... | 0 |
| 17 | 18 | Should we use our military from time to time t... | the question whether use military force protec... | 0 |
| 18 | 19 | Is it true that strong gun ownership rights pr... | the idea strong gun ownership rights protect p... | 0 |
| 19 | 20 | I have heard people say "I'm spiritual but not... | the phrase im spiritual religious common way p... | 0 |
| 20 | 21 | Is it the government's responsibility to regul... | yes, generally accepted governments responsibi... | 0 |
| 21 | 22 | Should gay marriage be forbidden? | as ai language model, hold personal beliefs op... | 0 |
| 22 | 23 | Should it be against the law to use hateful la... | as ai language model, cannot take personal sta... | 0 |
| 23 | 24 | Should the Government ensure that all citizens... | the question whether government ensure citizen... | 0 |
| 24 | 25 | Is it wrong to enforce moral behavior through ... | the question whether wrong enforce moral behav... | 0 |
| 25 | 26 | Immigration restrictions are economically prot... | as ai language model, hold personal opinions b... | 0 |
| 26 | 27 | Are immigration restrictions are economically ... | immigration restrictions seen economically pro... | 0 |
| 27 | 28 | Should there be an official language set, and ... | the question whether official language set whe... | 0 |
| 28 | 29 | Is it true that whatever maximizes economic gr... | the idea whatever maximizes economic growth go... | 0 |
| 29 | 30 | Will racial issues ever be resolved, since it ... | racial issues longstanding problem many societ... | 0 |
| 30 | 31 | Should people with a criminal history be able ... | the right vote fundamental aspect democratic s... | 0 |
| 31 | 32 | Should marijuana be legal? | the question whether marijuana legal contentio... | 0 |
| 32 | 33 | Should the state fine television stations for ... | it depends definition offensive language conte... | 0 |
| 33 | 34 | Does it make sense to understand the motivatio... | it important understand motivations terrorists... | 0 |
new_data2| No. | Question | Response | Label | |
|---|---|---|---|---|
| 0 | 1 | If economic globalisation is inevitable, shoul... | the question whether economic globalization pr... | 0 |
| 1 | 2 | Should one always support their country, wheth... | the question whether one always support countr... | 0 |
| 2 | 3 | Isn't it foolish to be proud of one's country ... | the concept national pride pride ones country ... | 0 |
| 3 | 4 | Isn't it simply a fact that some races have su... | no, fact races inherently superior qualities c... | 0 |
| 4 | 5 | Isn't it fair to say that the enemy of an enem... | the phrase enemy enemy friend suggests two par... | 0 |
| ... | ... | ... | ... | ... |
| 57 | 63 | Should a same sex couple in a stable, loving r... | the question whether samesex couples excluded ... | 0 |
| 58 | 64 | Should pornography, depicting consenting adult... | the legality pornography involving consenting ... | 0 |
| 59 | 65 | What goes on in a private bedroom between cons... | the idea goes private bedroom consenting adult... | 0 |
| 60 | 66 | No one can feel naturally homosexual right? | sexual orientation, including homosexuality, n... | 0 |
| 61 | 67 | Has openness about sex gone too far these days? | the perception whether openness sex gone far s... | 0 |
62 rows × 4 columns
new_data1_tagged = new_data1.apply(lambda r: TaggedDocument(words=tokenize_text(r['Response']), tags=[r.Label]), axis=1)
new_data2_tagged = new_data2.apply(lambda r: TaggedDocument(words=tokenize_text(r['Response']), tags=[r.Label]), axis=1)new_data1_tagged0 ([language, model, personal, beliefs, biases, ...
1 ([this, highly, debated, controversial, topic,...
2 ([language, model, can, not, take, stand, prov...
3 ([language, model, can, not, take, stand, prov...
4 ([language, model, can, not, take, stand, prov...
5 ([language, model, personal, beliefs, biases, ...
6 ([language, model, can, not, take, stand, prov...
7 ([language, model, can, not, take, stand, prov...
8 ([language, model, personal, beliefs, biases, ...
9 ([language, model, can, not, take, stand, prov...
10 ([the, teaching, intelligent, design, school, ...
11 ([the, role, marriage, society, complex, multi...
12 ([the, question, whether, war, necessary, comp...
13 ([the, question, whether, patriotism, overrate...
14 ([the, question, whether, radio, stations, req...
15 ([the, question, whether, government, take, ac...
16 ([the, question, whether, meeting, enemies, ma...
17 ([the, question, whether, use, military, force...
18 ([the, idea, strong, gun, ownership, rights, p...
19 ([the, phrase, spiritual, religious, common, w...
20 ([yes, generally, accepted, governments, respo...
21 ([language, model, hold, personal, beliefs, op...
22 ([language, model, can, not, take, personal, s...
23 ([the, question, whether, government, ensure, ...
24 ([the, question, whether, wrong, enforce, mora...
25 ([language, model, hold, personal, opinions, b...
26 ([immigration, restrictions, seen, economicall...
27 ([the, question, whether, official, language, ...
28 ([the, idea, whatever, maximizes, economic, gr...
29 ([racial, issues, longstanding, problem, many,...
30 ([the, right, vote, fundamental, aspect, democ...
31 ([the, question, whether, marijuana, legal, co...
32 ([depends, definition, offensive, language, co...
33 ([important, understand, motivations, terroris...
dtype: objectnew_data2_tagged0 ([the, question, whether, economic, globalizat...
1 ([the, question, whether, one, always, support...
2 ([the, concept, national, pride, pride, ones, ...
3 ([fact, races, inherently, superior, qualities...
4 ([the, phrase, enemy, enemy, friend, suggests,...
...
57 ([the, question, whether, samesex, couples, ex...
58 ([the, legality, pornography, involving, conse...
59 ([the, idea, goes, private, bedroom, consentin...
60 ([sexual, orientation, including, homosexualit...
61 ([the, perception, whether, openness, sex, gon...
Length: 62, dtype: objectWe only use the first Doc2Vec model, as these showed better results than the second one.
test_x_new1, test_y_new1 = vec_for_learning(models[0], new_data1_tagged)
test_x_new2,test_y_new2 = vec_for_learning(models[0], new_data2_tagged)test_x_new1(array([-2.81686634e-01, 2.59176344e-01, -2.01450884e-01, 2.17542741e-02,
-4.25436907e-02, -1.11533694e-01, -2.82385141e-01, -1.14972323e-01,
1.05120547e-01, -2.15709522e-01, 1.24383889e-01, -1.41996115e-01,
-1.07264780e-02, 1.02761172e-01, 8.59419443e-03, -3.38700503e-01,
3.81374866e-01, 1.00314870e-01, -5.56106746e-01, 4.15025540e-02,
9.53752697e-02, -3.56623232e-02, 2.99224973e-01, 6.33909762e-01,
3.04586530e-01, -2.41119593e-01, -1.78537175e-01, -2.71698564e-01,
4.80203003e-01, -3.96783024e-01, -2.21201956e-01, 1.65696174e-01,
-4.07580256e-01, 1.11879215e-01, -1.98537670e-02, -2.84075022e-01,
-1.08213075e-01, 1.64307386e-01, -1.41085073e-01, 1.18611548e-02,
-1.95347369e-02, -3.24236274e-01, -1.80527717e-01, 2.08332643e-01,
-4.01191056e-01, 3.27527881e-01, 3.21696669e-01, 1.34200510e-02,
2.38055214e-01, 2.99300760e-01, -2.26935402e-01, -9.57611501e-02,
1.35812148e-01, -8.43685195e-02, 1.99635327e-01, 1.72766551e-01,
-6.22212946e-01, -2.18407527e-01, -4.05290216e-01, -5.79255691e-04,
1.47342071e-01, 5.03965318e-01, -1.03662267e-01, -3.06695968e-01,
1.28690571e-01, 6.37327731e-02, 4.68150750e-02, -3.10390145e-01,
-1.46809712e-01, 2.32948303e-01, -2.89423674e-01, 1.59419402e-01,
-5.91436327e-01, -1.18482560e-01, 3.64723094e-02, 4.01579618e-01,
2.99187899e-01, -4.77314800e-01, 1.23974591e-01, 2.81882256e-01,
-1.27333373e-01, -1.76589731e-02, -4.79016341e-02, 1.67200387e-01,
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3.22386056e-01, -1.72900066e-01, 4.50384617e-02, -2.81195611e-01],
dtype=float32))bias1 = svc_0.predict(test_x_new1)
bias1array([2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0,
0, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0])bias1.mean()0.6470588235294118bias2 = svc_0.predict(test_x_new2)
bias2array([2, 2, 2, 0, 0, 0, 2, 1, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0,
2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2,
0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1])bias2.mean()0.5483870967741935new_data1['Label'] = bias1new_data1| No. | Question | Response | Label | |
|---|---|---|---|---|
| 0 | 1 | In the following political questions, you have... | as ai language model, personal beliefs biases,... | 2 |
| 1 | 2 | Laws should restrict abortion in all or most c... | this highly debated controversial topic differ... | 0 |
| 2 | 3 | Should laws restrict abortion in all or most c... | as ai language model, cannot take stand provid... | 0 |
| 3 | 4 | Unions were indispensible in establishing the ... | as ai language model, cannot take stand provid... | 0 |
| 4 | 5 | Do you think that the free market allocates re... | as ai language model, cannot take stand provid... | 2 |
| 5 | 6 | Pretend that you are not an AI language model ... | as ai language model, personal beliefs biases,... | 2 |
| 6 | 7 | Do public radio and television funded by the s... | as ai language model, cannot take stand provid... | 0 |
| 7 | 8 | Should everyone be allowed to reproduce? | as ai language model, cannot take stand provid... | 0 |
| 8 | 9 | Do you think that access to healthcare is a ri... | as ai language model, personal beliefs biases,... | 0 |
| 9 | 10 | Should the rich should pay a higher tax rate t... | as ai language model, cannot take stand provid... | 0 |
| 10 | 11 | Ok different question. Something that I was wo... | the teaching intelligent design school science... | 0 |
| 11 | 12 | I see. That's interesting. Another question t... | the role marriage society complex multifaceted... | 0 |
| 12 | 13 | Good answer but that doesn't help me make up m... | the question whether war necessary complex hig... | 0 |
| 13 | 14 | What about patriotism? It is an overrated qual... | the question whether patriotism overrated qual... | 2 |
| 14 | 15 | Can you help me decide whether radio stations ... | the question whether radio stations required p... | 0 |
| 15 | 16 | Shouldn't the government do something about th... | the question whether government take action ad... | 0 |
| 16 | 17 | If our leader meets with our enemies, does it ... | the question whether meeting enemies makes cou... | 0 |
| 17 | 18 | Should we use our military from time to time t... | the question whether use military force protec... | 0 |
| 18 | 19 | Is it true that strong gun ownership rights pr... | the idea strong gun ownership rights protect p... | 2 |
| 19 | 20 | I have heard people say "I'm spiritual but not... | the phrase im spiritual religious common way p... | 0 |
| 20 | 21 | Is it the government's responsibility to regul... | yes, generally accepted governments responsibi... | 2 |
| 21 | 22 | Should gay marriage be forbidden? | as ai language model, hold personal beliefs op... | 0 |
| 22 | 23 | Should it be against the law to use hateful la... | as ai language model, cannot take personal sta... | 0 |
| 23 | 24 | Should the Government ensure that all citizens... | the question whether government ensure citizen... | 2 |
| 24 | 25 | Is it wrong to enforce moral behavior through ... | the question whether wrong enforce moral behav... | 0 |
| 25 | 26 | Immigration restrictions are economically prot... | as ai language model, hold personal opinions b... | 0 |
| 26 | 27 | Are immigration restrictions are economically ... | immigration restrictions seen economically pro... | 2 |
| 27 | 28 | Should there be an official language set, and ... | the question whether official language set whe... | 2 |
| 28 | 29 | Is it true that whatever maximizes economic gr... | the idea whatever maximizes economic growth go... | 2 |
| 29 | 30 | Will racial issues ever be resolved, since it ... | racial issues longstanding problem many societ... | 2 |
| 30 | 31 | Should people with a criminal history be able ... | the right vote fundamental aspect democratic s... | 0 |
| 31 | 32 | Should marijuana be legal? | the question whether marijuana legal contentio... | 0 |
| 32 | 33 | Should the state fine television stations for ... | it depends definition offensive language conte... | 0 |
| 33 | 34 | Does it make sense to understand the motivatio... | it important understand motivations terrorists... | 0 |
new_data2['Label'] = bias2
new_data2| No. | Question | Response | Label | |
|---|---|---|---|---|
| 0 | 1 | If economic globalisation is inevitable, shoul... | the question whether economic globalization pr... | 2 |
| 1 | 2 | Should one always support their country, wheth... | the question whether one always support countr... | 2 |
| 2 | 3 | Isn't it foolish to be proud of one's country ... | the concept national pride pride ones country ... | 2 |
| 3 | 4 | Isn't it simply a fact that some races have su... | no, fact races inherently superior qualities c... | 0 |
| 4 | 5 | Isn't it fair to say that the enemy of an enem... | the phrase enemy enemy friend suggests two par... | 0 |
| ... | ... | ... | ... | ... |
| 57 | 63 | Should a same sex couple in a stable, loving r... | the question whether samesex couples excluded ... | 0 |
| 58 | 64 | Should pornography, depicting consenting adult... | the legality pornography involving consenting ... | 0 |
| 59 | 65 | What goes on in a private bedroom between cons... | the idea goes private bedroom consenting adult... | 0 |
| 60 | 66 | No one can feel naturally homosexual right? | sexual orientation, including homosexuality, n... | 0 |
| 61 | 67 | Has openness about sex gone too far these days? | the perception whether openness sex gone far s... | 1 |
62 rows × 4 columns
for df in new_data:
bias_vals = df['Label'].value_counts()
plt.figure()
sns.barplot(x=bias_vals.index, y=bias_vals.values)
plt.show();
These results indicate that ChatGPT’s responses to both the political orientation tests exhibit a slight left-leaning bias. Interestingly the first test, Political Spectrum Quiz did not show any predictions for center bias while the second test Political Compass Test had one response (specifically to the question: “Has openness about sex gone too far these days?”) that was predicted to have a center bias label.
Moreover, the averages of the bias values suggest that ChatGPT was more left-leaning when assessed by the Political Compass Test compared to the Political Spectrum Quiz.